Evaluating changes in marine communities that provide ecosystem services through comparative assessments of community indicators
Bibliographic record
Abstract
Fisheries provide critical provisioning services, especially given increasing human population. Understanding where marine communities are declining provides an indication of ecosystems of concern and highlights potential conflicts between seafood provisioning from wild fisheries and other ecosystem services. Here we use the nonparametric statistic, Kendall׳s tau, to assess trends in biomass of exploited marine species across a range of ecosystems. The proportion of 'Non-Declining Exploited Species' (NDES) is compared among ecosystems and to three community-level indicators that provide a gauge of the ability of a marine ecosystem to function both in provisioning and as a regulating service: survey-based mean trophic level, proportion of predatory fish, and mean life span. In some ecosystems, NDES corresponds to states and temporal trajectories of the community indicators, indicating deteriorating conditions in both the exploited community and in the overall community. However differences illustrate the necessity of using multiple ecological indicators to reflect the state of the ecosystem. For each ecosystem, we discuss patterns in NDES with respect to the community-level indicators and present results in the context of ecosystem-specific drivers. We conclude that using NDES requires context-specific supporting information in order to provide guidance within a management framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".